hf-fetch-model

Download HuggingFace models at full speed with a single function call.

Features

• Single-file download — download one file by name, get its cache path
• Repo-level download — give it a model ID, get all files
• Maximum throughput — multi-connection parallel Range downloads for large files (≥100 MiB, 8 connections by default) enabled for all download functions, plus hf-hub's .high() mode
• Download diagnostics — structured tracing events at debug level report download plan, per-file chunked/single decisions, throughput, and completion summary
• File filtering — glob patterns (*.safetensors) and presets (safetensors, gguf, config-only)
• HF cache compatible — files stored in ~/.cache/huggingface/hub/
• Progress reporting — per-file callbacks, optional indicatif progress bars
• Checksum verification — SHA256 against HuggingFace LFS metadata
• Retry with backoff — exponential backoff + jitter for flaky connections
• Timeout control — per-file and overall time limits
• Cache diagnostics — status command shows per-file download state (complete / partial / missing)
• Model search — search command queries the HuggingFace Hub by keyword
• CLI included — hf-fetch-model / hf-fm binary for command-line use

Installation

Library

cargo add hf-fetch-model

CLI

cargo install hf-fetch-model --features cli

This installs two binaries: hf-fetch-model (explicit) and hf-fm (short alias).

Quick Start (Library)

// Minimal — download everything
let path = hf_fetch_model::download("google/gemma-2-2b-it".to_owned()).await?;

// Configured — filter + progress
use hf_fetch_model::{FetchConfig, Filter};

let config = Filter::safetensors()
    .on_progress(|e| println!("{}: {:.1}%", e.filename, e.percent))
    .build()?;

let path = hf_fetch_model::download_with_config(
    "google/gemma-2-2b-it".to_owned(),
    &config,
).await?;

Blocking wrappers (download_blocking(), download_with_config_blocking()) are available for non-async callers.

Single-file download

use hf_fetch_model::FetchConfig;

let config = FetchConfig::builder()
    .on_progress(|e| println!("{}: {:.1}%", e.filename, e.percent))
    .build()?;

// Download one file — returns the local cache path
let path = hf_fetch_model::download_file(
    "mntss/clt-gemma-2-2b-426k".to_owned(),
    "W_enc_0.safetensors",
    &config,
).await?;

// Blocking variant for non-async callers
let path = hf_fetch_model::download_file_blocking(
    "mntss/clt-gemma-2-2b-426k".to_owned(),
    "config.yaml",
    &config,
)?;

CLI Usage

# Download all files
hf-fetch-model google/gemma-2-2b-it

# Download safetensors + config only
hf-fm google/gemma-2-2b-it --preset safetensors

# Custom filters
hf-fm google/gemma-2-2b-it --filter "*.safetensors" --filter "*.json"

# Download to a specific directory
hf-fm google/gemma-2-2b-it --output-dir ./models

# Download a single file
hf-fm download-file mntss/clt-gemma-2-2b-426k W_dec_0.safetensors

# Search for models on HuggingFace Hub
hf-fm search RWKV-7

# Check download status (per-repo or entire cache)
hf-fm status RWKV/RWKV7-Goose-World3-1.5B-HF
hf-fm status

# List model families in local cache
hf-fm list-families

# Discover new families from HuggingFace Hub
hf-fm discover

# Download with diagnostics (chunked/single decisions, throughput)
hf-fm google/gemma-2-2b-it -v

Subcommands

<ARG> = required, [ARG] = optional.

Download Flags

These flags apply to the default download command (hf-fm <REPO_ID>) and download-file.

General Flags

Subcommands accept their own flags (e.g., --limit for search and discover). Run hf-fm <command> --help for details.

Download Diagnostics

hf-fetch-model emits structured tracing events at debug level to help diagnose download performance. In the CLI, use the --verbose / -v flag. For library users, initialize a tracing-subscriber at debug level (e.g., RUST_LOG=hf_fetch_model=debug):

# CLI — verbose flag (prints diagnostics to stderr)
hf-fm google/gemma-2-2b-it -v

Example output:

DEBUG hf_fetch_model: listing repository files repo_id="allenai/OLMo-1B-hf"
DEBUG hf_fetch_model: metadata fetch succeeded files_with_size=8 total_files=8
DEBUG hf_fetch_model: download plan total_files=8 concurrency=4 connections_per_file=8 chunk_threshold_mib=100 chunked_enabled=true
DEBUG hf_fetch_model: chunked download (multi-connection) filename="model.safetensors" size_mib=2475 connections=8
DEBUG hf_fetch_model: single-connection download (below chunk threshold) filename="config.json" size_mib=0
DEBUG hf_fetch_model: download complete filename="model.safetensors" elapsed_secs="23.1" throughput_mbps="857.2"
DEBUG hf_fetch_model: download complete files_downloaded=8 files_failed=0 total_elapsed_secs="24.3"

Key diagnostics:

• "metadata fetch failed" (warning): file sizes are unknown, so chunked downloads are disabled — all files use single-connection download.
• "single-connection download" with reason "file size unknown": metadata was not available for this file.
• "chunked download": file exceeds chunk_threshold and is being downloaded with multiple parallel HTTP Range connections.
• throughput_mbps: actual per-file throughput, useful for comparing single vs chunked performance.

Architecture

candle-mi
  download_model() convenience fn
         │ optional dep (feature = "fast-download")
hf-fetch-model
  • repo file listing
  • file filtering (glob patterns)
  • parallel file orchestration
  • multi-connection Range downloads (large files)
  • progress callbacks
  • checksum verification
  • resume / retry
  • cache diagnostics & model search
         │ dep
hf-hub (tokio, .high())
  • single-connection download (.high() mode)
  • HF cache layout compatibility
  • auth token handling

Configuration

Used by

• candle-mi — Mechanistic interpretability toolkit for transformer models, uses hf-fetch-model for fast model downloads (optional fast-download feature).

License

Licensed under either of Apache License, Version 2.0 or MIT License at your option.